Discriminant analysis as a method of predicting mineral occurrence potentials in central Norway

Abstract

The statistical technique of discriminant analysis is used to define target areas for detailed general exploration given only general geological information and aeromagnetic anomaly blues. In the test area, located in Central Norway, on-going exploration surveys have revealed the presence of mineralization; however, it still has not been determined if any of the sites will beeconomically feasible. The area was divided into 1400 1-km × 1-km cells by superimposing square grid on 1:50,000-scale geological and geophysical maps. Later the area was divided into two subareas based on major differences in each area's geology. A number of geological natures and the aeromagnetic anomaly values were coded systematically in each cell. The cells representing an advanced degree of exploration were chosen as control cells in each of the subareas. The geological and geophysical parameters were transformed, by means of relatively simple transformations, to produce near-normal frequency distributions. A discriminant function was then obtained by discriminant analysis to divide the control data into two groups, cells with presence of mineral occurrence and cells without mineral occurrence. the discriminant function obtained for the control area proved to be relevant both geologically and statistically. Consequently, the discriminant equation was applied to cells outside the control area. The cells were assigned to one of the two groups by entering the geologic factors pleasured from the maps into the discriminant model. The exploration potential of a large number of cells was evaluated by this procedure. To test the results, field work including geochemical sampling was carried out in the cells with highest probability of mineral occurrance The field work results have shown that the application of discriminant analysis to geological information at 1:50,000 scale with 1-km × 1-km cells combined with a careful selection of techniques for transforming the variables is a feasible method for predicting gaeralization, and as such could become a valuable tool for mining exploration.